There exist systems that accomplish automatic verification or identification of a person using his/her fingerprint. A fingerprint of a person comprises a distinctive and unique ridge pattern structure. For identification/verification purposes, ridge pattern structure could be characterized by endings and bifurcations of the individual ridges. These features are popularly known as minutiae. An example fingerprint is shown in FIG. 1A; the minutiae for the fingerprint shown in FIG. 1A are shown in FIG. 1B as being enclosed by "boxes." For example, box 101B shows a bifurcation minutia of a bifurcated ridge 101A in FIG. 1A and box 103B shows a ridge ending minutia of ridge 103A in FIG. 1A. Note that minutiae on the ridges in fingerprints have directions (also called orientations) 105 associated with them. The direction of the minutia at a ridge end 103B is the direction in which the end of the ridge points. The direction of a bifurcation minutia 101B is the direction in which the bifurcated ridge points. Minutiae also have locations which are the positions of the minutiae on the fingerprint with respect to some coordinate system.
The prevalent methods of fingerprint identification and verification methods are based on minutiae features. These systems need to process the fingerprint images to obtain accurate and reliable minutiae features to effectively determine or verify the identity of a person.
FIG. 2 is a flow chart showing the steps generally performed by a typical prior art system 200.
In step 210, the image is acquired. This acquisition of the image could either be through a CCD camera and framegrabber interface or through a document scanner communicating with the primary computing equipment.
Once the image is acquired into the computer memory or onto disk, relevant features minutia features are extracted (220). Not all of the features thus extracted are reliable. Some of the unreliable features are optionally pruned (step 230), e.g., manually edited. The resultant reliable features are used for matching two fingerprint images (step 240). That is, matching the acquired fingerprint image with stored minutiae representations of previously acquired fingerprint images.
In semi-automatic systems, the unreliable features could be manually pruned by visual inspection of the processed fingerprint images by a human expert before the minutiae are used for matching (step 240). The fingerprint feature extraction, pruning, and matching system constitute the primary backbone of a typical minutiae-based Automatic Fingerprint Identification Systems (AFIS). The matching results are typically verified by a human expert (step 260 in FIG. 2). The verification may also be performed automatically. The following reference describes examples of the state of the prior art:
Nalini K. Ratha and Shaoyun Chen and Anil K. Jain, Adaptive flow orientation based texture extraction in fingerprint images, Pattern Recognition, vol. 28, no. 11, pp. 1657-1672, November, 1995.
This reference is incorporated herein by reference in its entirety.
FIG. 3 is a flow chart showing the prior art steps performed by a feature extraction process that are similar to some of the feature extraction proposed by Ratha, Jain, and Chen in the above mentioned article.
It is often not desirable to directly use the input fingerprint image for feature extraction. The fingerprint image might need an enhancement or preprocessing before one could further extract minutiae. Typically, an image smoothing process is employed to reduce the pixel-wise noise by assuming spatial pixel correlation (step 305).
After the preprocessing stages, prior art systems find the directions of the ridge flow (step 310). The next important step in the processing is finding the exact location of the finger in the image, a process referred to as the foreground/ background segmentation (step 315). Once the finger is localized, the next step is to extract the ridges from the finger image (step 320). The ridges thus extracted are thick and might contain some noisy artifacts which do not correspond to any meaningful structures on the finger. The small structures could be safely removed (step 325). The longer structures are thinned to one-pixel width and then processed to remove any other artifacts using morphological operators (step 330). The locations and orientations of ridge endings and bifurcations are then extracted from the thinned structures (step 335) to obtain the minutiae. In some systems, post-processing 340 is performed on the extracted minutiae.